---
id: 20260708-T0-03
title: "混合块扩散语言模型：并行生成长文本，速度提升"
title_en: "Hybrid Block Diffusion LM Enables Parallel Long-Context Generation"
url: https://ai.daily.yangsir.net/daily/20260708-T0-03
issue_date: 2026-07-08
publish_date: 2026-07-07T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2607.02805
---

# 混合块扩散语言模型：并行生成长文本，速度提升

arXiv新论文提出混合块扩散语言模型（Hybrid Block Diffusion LM），结合部分双向性，实现长文本的高吞吐量生成。传统自回归模型逐token生成受限于内存带宽，而该方法通过并行解码显著提升速度。实验表明，在保持生成质量的同时，长上下文生成的吞吐量大幅提高，适合文档生成和代码合成等场景。

## English Version

**Hybrid Block Diffusion LM Enables Parallel Long-Context Generation**

A new arXiv paper proposes a Hybrid Block Diffusion Language Model with partial bidirectionality, enabling high-throughput long-context generation. Unlike autoregressive models limited by memory bandwidth, this method uses parallel decoding to significantly boost speed. Experiments show improved throughput for long-context generation while maintaining quality, suitable for document and code generation.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2607.02805)

**详情页**：https://ai.daily.yangsir.net/daily/20260708-T0-03

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